[2604.01365] VIANA: character Value-enhanced Intensity Assessment via domain-informed Neural Architecture

Physics > Chemical Physics arXiv:2604.01365 (physics) [Submitted on 1 Apr 2026] Title:VIANA: character Value-enhanced Intensity Assessment via domain-informed Neural Architecture Authors:Luana P. Queiroz, Icaro S. C. Bernardes, Ana M. Ribeiro, Bernardo M. Aguilera-Mercado, Idelfonso B. R. Nogueira View a PDF of the paper titled VIANA: character Value-enhanced Intensity Assessment via domain-informed Neural Architecture, by Luana P. Queiroz and 4 other authors View PDF HTML (experimental) Abstract:Predicting the perceived intensity of odorants remains a fundamental challenge in sensory science due to the complex, non-linear behavior of their response, as well as the difficulty in correlating molecular structure with human perception. While traditional deep learning models, such as Graph Convolutional Networks (GCNs), excel at capturing molecular topology, they often fail to account for the biological and perceptual context of olfaction. This study introduces VIANA, a novel "tri-pillar" framework that integrates structural graph theory, character value embeddings, and phenomenological behavior. This methodology systematically evaluates knowledge transfer across three distinct domains: molecular structure via GCNs, semantic odor character values via Principal Odor Map (POM) embeddings, and biological dose-response logic via Hill's law. We demonstrate that knowledge transfer is not inherently positive; rather, a balance must be maintained in the volume of information provided ...